This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Bernstein, R. S. Articles by Naamara, W. Search for Related Content

Simulating the Control of a Heterosexual HIV Epidemic in a Severely Affected East African City

UNICEF JAKARTA, PO Box 5747, Grand Central Station, New York, New York 10163-5747
Family Health International, PO Box 13950, Research Triangle Park, North Carolina 27709
University of Illinois, 505 East Green Street, Champaign, Illinois 61820
Hospicio Nationale De St-Maurice, 94415 Saint-Maurice, France
The Futures Group International, 1050 17th Street, Washington, DC 20036
PO Box 1646, Kigali, Rwanda

We compared three intervention strategies for preventing heterosexual transmission of the human immunodeficiency virus (HIV) using deterministic and stochastic models to simulate the epidemic of acquired immunodeficiency syndromes (AIDS). We estimated demographic, biological, and behavioral parameters for a severely affected east African city early in the epidemic, and used these parameter values in computing the spread of HIV under five scenarios: (1) a baseline scenario with no public health interventions; three single-intervention scenarios with strategies to (2) reduce the number and rate of change of sex partners, (3) increase condom use, or (4) improve treatment of sexually transmitted diseases (STD); and a (5) combined-intervention scenario. The rankings were the same in both models—decreasing partner change was most effective, followed by condom use and STD treatment. Combined interventions were more effective than single interventions. They interacted to produce impacts that varied with the trajectory of the epidemic at the onset of the interventions. Their timely, targeted, and sustained implementation appears critical to slow the epidemic significantly.

Key Words: simulation; applications; health care; epidemiology